Image signatures are image features for discriminating images (determining identity). By comparing an image signature extracted from an image with an image signature extracted from another image, an identity scale (in general, referred to as similarity or distance) indicating a degree of the two images being identical can be calculated from a comparison result. Further, by comparing the calculated identity scale with a threshold, it is possible to determine whether or not the two images are identical. In this context, the meaning of “two images being identical” includes not only the case where the two images are identical at the level of image signals (pixel values of pixels constituting the images), but also the case where one image is a duplicate image of the other by means of various alteration processes such as conversion of compression format of an image, conversion of size/aspect ratio of an image, adjustment of color tone of an image, various filtering processes (sharpening, smoothing, and the like) applied to an image, local processing (caption superimposition, cutout, and the like) applied to an image, and recapturing of an image. By using image signatures, as it is possible to detect duplication of an image or a moving image which is a collection of images, for example, image signatures are applicable to an illegal copy detection system for images or moving images.
An image signature is generally formed of a collection of features. Given that each of the features included in a collection is a dimension, an image signature is composed of feature vectors of multiple dimensions. In particular, a quantization index (quantized value), which is a discrete value, is often used as a feature. Examples of image signatures are described in Non-Patent Document 1, Non-Patent Document 2, and Patent Document 1. In the methods described in those documents, features are extracted for a plurality of local regions of an image, the extracted features are quantized to obtain quantization indexes, and the calculated quantization indexes for the respective local regions constitute quantization index vectors to serve as image signatures.
Specifically, in Non-Patent Document 1 and Non-Patent Document 2, an image is divided into blocks. Each of the blocks is used as a local region, and a feature (quantization index) is extracted. Further, in Non-Patent Document 1, luminance distribution patterns within a block are classified into eleven types and are used as quantization indexes. In Non-Patent Document 2 (art described as “Local Edge Representation” in Non-Patent Document 2), a position of center of gravity of an edge point, extracted from a block, is quantized to be used as a quantization index.
On the other hand, as shown in FIG. 5, the method described in Patent Document 1 includes respectively calculating mean luminance values from thirty two pieces of rectangle regions 244 (among them, sixteen pieces of rectangle regions are shown in FIG. 5) at predetermined positions in an image 240, and calculating differences in mean luminance value between rectangle regions forming pairs (the paired rectangle regions are linked to each other with dotted lines 248 in FIG. 5), to thereby obtain a difference vector 250 in sixteen dimensions. With respect to the difference vector 250, a composite vector is generated by means of vector transformation, and a quantization index vector in sixteen dimensions, acquired by quantizing the respective dimensions of the composite vector, is used as an image signature.
When designing such an image signature formed of a collection of features, selecting features to be used (what types of parameters are used for feature extraction) is important because it determines performance (accuracy of determining identity of images) of the image signature. In an image signature formed of a collection of features, performance of the image signature can be improved by appropriately selecting the features.
As such, it is important to select features suitable for (optimizing the performance of) an image signature formed of a collection of features (that is, features enabling high determination accuracy of identity of images).
Regarding the image signatures described in Non-Patent Document 1, Non-Patent Document 2, and Patent Document 1, each of the features is extracted from a local region determined for each feature (different from each other). As such, in the examples of those documents, performance of the image signature is determined depending on a local area from which the feature is extracted (what kind of local area is set for each feature).
In general, when designing an image signature formed of a collection of features, determination (selection) of the features (parameters for extracting features) has often been performed according to empirical knowledge or trial and error experiments. For example, in Non-Patent Documents 1 and 2, a local region for each of the features is a block formed by regularly dividing an image. In Non-Patent Document 1, for example, an image is regularly divided into 8*8=64 blocks, and each of the blocks is used as a local region to extract a feature. However, it is difficult to optimize performance of an image signature (accuracy in determining identity of images) by such empirical knowledge or trial and error experiments.
Meanwhile, art of automatically selecting features to optimize performance (referred to as a feature selection technique) is used in the field of pattern recognition. Specifically, methods using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been known.